Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures
Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10023499/ |
| _version_ | 1828048402847367168 |
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| author | Irfan Ali Kandhro Sultan M. Alanazi Fayyaz Ali Asadullah Kehar Kanwal Fatima Mueen Uddin Shankar Karuppayah |
| author_facet | Irfan Ali Kandhro Sultan M. Alanazi Fayyaz Ali Asadullah Kehar Kanwal Fatima Mueen Uddin Shankar Karuppayah |
| author_sort | Irfan Ali Kandhro |
| collection | DOAJ |
| description | Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases. |
| first_indexed | 2024-04-10T18:55:48Z |
| format | Article |
| id | doaj.art-7347519d62c04ce89462998f35d398ee |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T18:55:48Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7347519d62c04ce89462998f35d398ee2023-02-01T00:00:33ZengIEEEIEEE Access2169-35362023-01-01119136914810.1109/ACCESS.2023.323866410023499Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity InfrastructuresIrfan Ali Kandhro0Sultan M. Alanazi1https://orcid.org/0000-0002-8827-9290Fayyaz Ali2Asadullah Kehar3Kanwal Fatima4Mueen Uddin5https://orcid.org/0000-0003-1919-3407Shankar Karuppayah6https://orcid.org/0000-0003-4801-6370Department of Computer Science, Sindh Madressatul Islam University, Karachi, PakistanDepartment of Computer Science, Northern Border University, Arar, Saudi ArabiaDepartment of Software Engineering, Sir Syed University of Engineering and Technology, Karachi Sindh, PakistanInstitute of Computer Science, Shah Abdul Latif University, Khairpur, Karachi Sindh, PakistanDepartment of Computer Science, Sindh Madressatul Islam University, Karachi, PakistanCollege of Computing and Information Technology, University of Doha For Science and Technology, Doha, QatarNational Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Gelugor, Penang, MalaysiaComputer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.https://ieeexplore.ieee.org/document/10023499/CybersecurityInternet of Thingsintrusion detection system (IDS)anomaly detectionsecurity attacksdeep learning |
| spellingShingle | Irfan Ali Kandhro Sultan M. Alanazi Fayyaz Ali Asadullah Kehar Kanwal Fatima Mueen Uddin Shankar Karuppayah Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures IEEE Access Cybersecurity Internet of Things intrusion detection system (IDS) anomaly detection security attacks deep learning |
| title | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_full | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_fullStr | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_full_unstemmed | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_short | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_sort | detection of real time malicious intrusions and attacks in iot empowered cybersecurity infrastructures |
| topic | Cybersecurity Internet of Things intrusion detection system (IDS) anomaly detection security attacks deep learning |
| url | https://ieeexplore.ieee.org/document/10023499/ |
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